The present subject matter relates generally to energy modeling. More specifically, the present invention relates to a software platform for modeling and simulating an energy marketplace.
In recent years, various governmental agencies have issued environmental regulations and initiatives with the goal of creating a clean energy future. A number of these regulations and initiatives are geared toward reducing air pollutants (e.g. carbon dioxide) generated by power plants. The general goal of these initiatives and regulations is to reduce energy demand, directly control emissions, and switch to less emitting fuels (e.g. renewable fuels, nuclear energy, natural gas).
A clean energy future will require massive investment in new infrastructure, such as solar cells, nuclear power, and wind turbines—integrated with coal and natural gas power plants on a more resilient and intelligent grid that interacts with buildings and vehicles. Some estimates conclude that the United States will require over $2.1 trillion in energy infrastructure investments over the next 30 years in order to modernize the electric grid and develop cleaner sources of energy.
To achieve the goal of a clean energy future, one of the more important tools used in the energy sector is an energy modeling software platform, which is used to evaluate strategies, projects, plans, and policies pertaining to energy systems scenarios and alternatives. Such activities are commonly subjected to multiple viewpoints and opinions spanning multiple organizations, including energy companies, government regulators and policy-makers, consultants, researchers, and public interest groups.
An energy model generally consists of a system characterization data schema (SCDS) and at least one modeling algorithm. The SCDS is generally used to construct and describe scenarios consisting of hypothetical or virtual energy demand, supply, equipment, infrastructure, fuels, and markets. The modeling algorithm generally consists of formulas and computing programs used to project the performance of the modeled system in terms that are energetic (i.e. flow of energy supply and demand), financial (i.e. costs, rates, revenues, and/or expenses), and physical (i.e. quantities of fuel and exhaust emissions).
Previous energy planning and production software platforms were generally not ideal for large-scale national modeling for a variety of reasons some of which include the lack of balance between scalability and granularity, the complexity of the software, the lack of lack of data management and quality assurance, the lack of wide spread collaboration from all stakeholders
Previous energy simulation software platforms must inherently trade-off between the simulation scale (i.e. geographic, granularity) and efficacy; therefore, the software cannot be effectively used at a large scale and with high granularity. Previous software typically constrained the analysis to a limited size, such that the modeling algorithms could solve (i.e., compute the projection) in a reasonable amount of time. Previous tools typically, but not necessarily, computed the projection using an optimization algorithm that intended for the computer program to identify a “best” solution among a limited number of alternatives. This limitation was necessary so that the software could compute the projection in a reasonable time frame, with batch-runs frequently computed overnight. To keep computation manageable, the typical solution was to limit the geographic jurisdiction of the analysis.
While utility planning products are typically limited in geographic scale, national models are typically limited in granularity. The national tools simplify the characterization of infrastructure and flows of energy are limited and simplified (reduced to fewer numbers of hypothetical components), such as combining all regional hydro-electric facilities into a single large hypothetical hydro-electric modeling object that is parameterized with averaged characteristics. In other words, existing software must sacrifice scale and/or granularity—or—be inconvenienced by longer solve times or failed solve-attempts (exceed algorithmic computing capacity). Neither approach allows for a highly detailed simulation (e.g., of individual power plants) at national scale, such that the implications of national scenarios may be directly simulated across the real-world companies that actually own and operate the energy infrastructure.
Further, previous platforms were generally complex and expensive to purchase and initiate. Procurement and set up of these platforms generally took weeks, thus limiting the user-base to industry experts and those who could afford expensive software.
The complex and expensive nature of previous platforms created a situation such that the platforms were not amenable to stakeholder engagement. State and local economies are be greatly influenced by whether their efforts to “green” the power grid are effective and efficient. Technical and regulatory analysis for these investment decisions is typically performed in state regulatory forums using proprietary utility modeling software. The expense and complexity of previous software products prevented public interest stakeholders, policy researchers, academia, and the general public from directly participating in this important analysis and associated conversations.
Further, previous platforms had limited capacity for rigorous data management and quality assurance. The scaling limitations and complex nature of existing platforms means that a relatively few number of experts are engaged in simulating large power systems. As such, the market for utility products was generally limited to a relatively small number of industry and regulatory users and consultants, while national models are limited to academic and government researchers. The practical reality is that the data quality of inputs and results is made difficult by the sheer magnitude of data being reviewed by such few participants.
Additionally, previous software platforms did not support integrated multi-userism, but instead were used by single independent users and provided a singular modeling solution over the entirety of a defined system boundary. No software platform distributed and coordinates independent planning efforts in an integrated platform and no platform was capable of commercializing a virtual marketplace comprised of many future energy and climate policy scenarios. Further, no platform could be deployed to support multifunctional policy compliance analysis and reporting.
Existing platforms can generally be categorized into four families—long-term utility planning software, short-term production simulations, national modeling tools, and web calculators.
Previous long-term utility planning software was generally used only for evaluation up to regional (multi-state) scale. Long-term software lacked the ability to scale geographically while also maintaining high granularity. While some long-term software is currently used for national scale simulation, the software is not normally licensed for third-party use, but rather used as a proprietary consulting product. Further, the software is not multi-user deployable and requires external post-processing for fine grained details and is not made open or transparent to the public.
Alternatively, previous short-term production simulation software, was highly granular, but lacked the ability to scale geographically or temporally to long time frames while keeping the same level of granularity. Utility companies utilize hourly production models and/or power-flow simulation to perform comparable analysis. In general, the increased complexity of these tools, including the temporal effects of power plants, market operations, transmission, and distribution systems, prohibited these tools from being used for analysis over long time frames or large geographic scales. Hourly production simulation is also scale-limited and does not allow for networked multi-users.
Previous national modeling tools did not offer the granularity to simulate individual power plants with the specificity required by utilities or regulators. While a few commercial software programs existed that evaluated the impact of national energy policies and strategies, these platforms were often onerous and were typically managed by governmental and research institutions in conjunction with experienced consultants. Other software programs only provided a life-cycle assessment for transportation fuels.
While, numerous energy and environmental web calculators exist and provide easy access and some transparent data, but do not offer rigorous power sector modeling that would be considered acceptable for regulatory purposes.
Accordingly, there is a need for an energy planning system that provides a networked software platform that simulates energy systems on a large geographic scale with high granularity, while also enabling crowd sourced data management and quality assurance commensurate with magnitude of data inputs and outputs and efficient sharing of user-generated energy simulations, as described herein.